QC_merge_Justin_v2.R
jsong
2023-06-28
# title: "QC_Justin_Seurat"
# output: html_document
# date: "2023-06-28"
# changed sperm filter to more stringent, < 3 to == 0
library(SingleCellExperiment)## Loading required package: SummarizedExperiment## Loading required package: MatrixGenerics## Loading required package: matrixStats##
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## completesetwd("~/OneDrive - University of Florida/Schnitzler_Lab/Jingwei/Whitney/Manuscript/10X_/Justin2019")
# Create a Seurat object for each sample
for (file in c("3XPBS_filtered_feature_bc_matrix", "Seawater_filtered_feature_bc_matrix"
)){
seurat_data <- Read10X(data.dir = paste0("data/", file))
seurat_obj <- CreateSeuratObject(counts = seurat_data,
min.features = 100,
project = file)
assign(file, seurat_obj)
}
# Merge samples into a single Seurat object
merged_seurat <- merge(x=`3XPBS_filtered_feature_bc_matrix`,
y=Seawater_filtered_feature_bc_matrix,
add.cell.id = c("3XPBS", "Seawater"))
# Check that the merged object has the appropriate sample-specific prefixes
head(merged_seurat@meta.data)## orig.ident nCount_RNA
## 3XPBS_AAACCCAAGACATCAA-1 3XPBS_filtered_feature_bc_matrix 2673
## 3XPBS_AAACCCAAGGGTAATT-1 3XPBS_filtered_feature_bc_matrix 1366
## 3XPBS_AAACCCAAGGTGCGAT-1 3XPBS_filtered_feature_bc_matrix 6805
## 3XPBS_AAACCCACAGCTGTAT-1 3XPBS_filtered_feature_bc_matrix 2268
## 3XPBS_AAACCCACATCGGCCA-1 3XPBS_filtered_feature_bc_matrix 4069
## 3XPBS_AAACCCACATGAATCC-1 3XPBS_filtered_feature_bc_matrix 3287
## nFeature_RNA
## 3XPBS_AAACCCAAGACATCAA-1 710
## 3XPBS_AAACCCAAGGGTAATT-1 397
## 3XPBS_AAACCCAAGGTGCGAT-1 1460
## 3XPBS_AAACCCACAGCTGTAT-1 1147
## 3XPBS_AAACCCACATCGGCCA-1 755
## 3XPBS_AAACCCACATGAATCC-1 1037tail(merged_seurat@meta.data)## orig.ident nCount_RNA
## Seawater_TTTGGTTAGTTTCGGT-1 Seawater_filtered_feature_bc_matrix 598
## Seawater_TTTGGTTGTACTGACT-1 Seawater_filtered_feature_bc_matrix 2672
## Seawater_TTTGTTGAGTCCGTCG-1 Seawater_filtered_feature_bc_matrix 2268
## Seawater_TTTGTTGAGTGCTACT-1 Seawater_filtered_feature_bc_matrix 3112
## Seawater_TTTGTTGGTCAACACT-1 Seawater_filtered_feature_bc_matrix 660
## Seawater_TTTGTTGGTCCAAGAG-1 Seawater_filtered_feature_bc_matrix 4405
## nFeature_RNA
## Seawater_TTTGGTTAGTTTCGGT-1 432
## Seawater_TTTGGTTGTACTGACT-1 1466
## Seawater_TTTGTTGAGTCCGTCG-1 578
## Seawater_TTTGTTGAGTGCTACT-1 1224
## Seawater_TTTGTTGGTCAACACT-1 270
## Seawater_TTTGTTGGTCCAAGAG-1 1886### QC ###
# Add number of genes per UMI for each cell to metadata
merged_seurat$log10GenesPerUMI <- log10(merged_seurat$nFeature_RNA) / log10(merged_seurat$nCount_RNA)
# Compute percent mitochondrial gene ratio
merged_seurat$mitoRatio <- PercentageFeatureSet(object = merged_seurat, pattern = "^HyS0613.")
merged_seurat$mitoRatio <- merged_seurat@meta.data$mitoRatio / 100
# Create metadata data frame
metadata <- merged_seurat@meta.data
# Add cell IDs to metadata
metadata$cells <- rownames(metadata)
# Create sample column. this will be useful later when merging with other batches
metadata$sample <- rep("Live_cells", dim(merged_seurat)[2])
# Rename columns, "new.name = old.name"
metadata <- metadata %>%
dplyr::rename(seq_folder = orig.ident,
nUMI = nCount_RNA,
nGene = nFeature_RNA)
# Add metadata back to Seurat object
merged_seurat@meta.data <- metadata
# check
tail(merged_seurat@meta.data)## seq_folder nUMI nGene
## Seawater_TTTGGTTAGTTTCGGT-1 Seawater_filtered_feature_bc_matrix 598 432
## Seawater_TTTGGTTGTACTGACT-1 Seawater_filtered_feature_bc_matrix 2672 1466
## Seawater_TTTGTTGAGTCCGTCG-1 Seawater_filtered_feature_bc_matrix 2268 578
## Seawater_TTTGTTGAGTGCTACT-1 Seawater_filtered_feature_bc_matrix 3112 1224
## Seawater_TTTGTTGGTCAACACT-1 Seawater_filtered_feature_bc_matrix 660 270
## Seawater_TTTGTTGGTCCAAGAG-1 Seawater_filtered_feature_bc_matrix 4405 1886
## log10GenesPerUMI mitoRatio
## Seawater_TTTGGTTAGTTTCGGT-1 0.9491420 0.0000000000
## Seawater_TTTGGTTGTACTGACT-1 0.9239233 0.0000000000
## Seawater_TTTGTTGAGTCCGTCG-1 0.8230696 0.0008818342
## Seawater_TTTGTTGAGTGCTACT-1 0.8839812 0.0009640103
## Seawater_TTTGTTGGTCAACACT-1 0.8623252 0.0000000000
## Seawater_TTTGTTGGTCCAAGAG-1 0.8988996 0.0002270148
## cells sample
## Seawater_TTTGGTTAGTTTCGGT-1 Seawater_TTTGGTTAGTTTCGGT-1 Live_cells
## Seawater_TTTGGTTGTACTGACT-1 Seawater_TTTGGTTGTACTGACT-1 Live_cells
## Seawater_TTTGTTGAGTCCGTCG-1 Seawater_TTTGTTGAGTCCGTCG-1 Live_cells
## Seawater_TTTGTTGAGTGCTACT-1 Seawater_TTTGTTGAGTGCTACT-1 Live_cells
## Seawater_TTTGTTGGTCAACACT-1 Seawater_TTTGTTGGTCAACACT-1 Live_cells
## Seawater_TTTGTTGGTCCAAGAG-1 Seawater_TTTGTTGGTCCAAGAG-1 Live_cells# OPTIONAL:
#save(merged_seurat, file="data/merged_seurat.RData")
# Visualize the number of cell counts per sample
merged_seurat@meta.data %>%
ggplot(aes(x=sample, fill=sample)) +
geom_bar() +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
theme(plot.title = element_text(hjust=0.5, face="bold")) +
ggtitle("NCells")
# Visualize the number UMIs/transcripts per cell. if UMI is between 500-1000, cells should probably
# be sequenced more deeply.
merged_seurat@meta.data %>%
ggplot(aes(color=sample, x=nUMI, fill= sample)) +
geom_density(alpha = 0.2) +
scale_x_log10() +
theme_classic() +
ylab("Cell density") +
geom_vline(xintercept = 300)
# Visualize the distribution of genes detected per cell via histogram
merged_seurat@meta.data %>%
ggplot(aes(color=sample, x=nGene, fill= sample)) +
geom_density(alpha = 0.2) +
theme_classic() +
scale_x_log10() +
geom_vline(xintercept = 7000)
Two peaks. suggesting doublet problem. The first peak is mostly sperm
# Visualize the overall complexity of the gene expression by visualizing the genes detected per UMI (novelty score)
merged_seurat@meta.data %>%
ggplot(aes(x=log10GenesPerUMI, color = sample, fill=sample)) +
geom_density(alpha = 0.2) +
theme_classic() +
geom_vline(xintercept = 0.8)
# Visualize the distribution of mitochondrial gene expression detected per cell
merged_seurat@meta.data %>%
ggplot(aes(color=sample, x=mitoRatio, fill=sample)) +
geom_density(alpha = 0.2) +
scale_x_log10() +
theme_classic() +
geom_vline(xintercept = 0.2)## Warning: Transformation introduced infinite values in continuous x-axis## Warning: Removed 4816 rows containing non-finite values (`stat_density()`).
# Visualize the correlation between genes detected and number of UMIs and determine whether strong presence of cells with low numbers of genes/UMIs
merged_seurat@meta.data %>%
ggplot(aes(x=nUMI, y=nGene, color=mitoRatio)) +
geom_point() +
scale_colour_gradient(low = "gray90", high = "black") +
stat_smooth(method=lm) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
geom_vline(xintercept = 500) +
geom_hline(yintercept = 250) +
facet_wrap(~sample)## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation: colour
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Filter out low quality cells using selected thresholds - these will change with experiment
filtered_seurat <- subset(x = merged_seurat,
subset= (nUMI >= 300 & nUMI <=100000) &
(nGene >= 100 & nGene <7000) &
(log10GenesPerUMI > 0.80) & # could change
(mitoRatio < 0.20))
filtered_seurat # 9655 cells## An object of class Seurat
## 22034 features across 9655 samples within 1 assay
## Active assay: RNA (22034 features, 0 variable features)# Gene level filtering
# Extract counts
counts <- GetAssayData(object = filtered_seurat, slot = "counts")
# Output a logical matrix specifying for each gene on whether or not there are more than zero counts per cell
nonzero <- counts > 0
# Sums all TRUE values and returns TRUE if more than 10 TRUE values per gene
keep_genes <- Matrix::rowSums(nonzero) >= 10
# Only keeping those genes expressed in more than 10 cells
filtered_counts <- counts[keep_genes, ]
# Reassign to filtered Seurat object
filtered_seurat <- CreateSeuratObject(filtered_counts, meta.data = filtered_seurat@meta.data)
filtered_seurat # 9655, did not remove any genes## An object of class Seurat
## 15068 features across 9655 samples within 1 assay
## Active assay: RNA (15068 features, 0 variable features)# OPTIONAL:
#save(filtered_seurat, file="output/filtered_seurat.RData")# SCtransform, PCA, UMAP, Clus
filtered_seurat <- SCTransform(filtered_seurat, vst.flavor = "v2") %>%
RunPCA(npcs = 50) %>%
RunUMAP(dims = 1:15) %>%
FindNeighbors(reduction = "pca", dims = 1:15) %>%
FindClusters(resolution = 0.5) ##
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|======================================================================| 100%## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 9655
## Number of edges: 273732
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9504
## Number of communities: 21
## Elapsed time: 0 seconds# UMAP
DimPlot(filtered_seurat,label = T) + NoLegend() + NoAxes()
# check a few known marker genes, using sctransform normalized data
FeaturePlot(filtered_seurat, features = c("HyS0061.60", "HyS0050.7", #PCNA, #piwi
"HyS0008.263", "HyS0030.203", #Ncol1, Nematocilin
"HyS0041.99", "HyS0004.446", #chitinase,mucs
"HyS0085.53", "HyS0013.338", #ELAV, Rfamide
"HyS0017.253", "HyS0010.323"), #Wnt5, Pitx
order = T, min.cutoff = 'q10')## Warning in FeaturePlot(filtered_seurat, features = c("HyS0061.60", "HyS0050.7",
## : All cells have the same value (0.693147180559945) of HyS0010.323.
FeaturePlot(filtered_seurat, features = c("HyS0027.170","HyS0001.110")) # early, late sperm
#save(filtered_seurat, file="output/Filtered_seurat_PC50_dim15_res.0.5.RData")
# remove sperm clusters
Iwant <- subset(filtered_seurat, idents = c("8","0", "7","2","1","3","12","6"), invert = T)
DimPlot(Iwant)
# now remove cells with sperm marker expression
# HyS0027.170
# HyS0070.46
# HyS4524.1
# HyS0007.253
# HyS0001.110
Iwant <- subset(Iwant, subset = HyS0027.170 > 0, invert = T )
Iwant <- subset(Iwant, subset = HyS0070.46 > 0, invert = T )
Iwant <- subset(Iwant, subset = HyS4524.1 > 0, invert = T )
Iwant <- subset(Iwant, subset = HyS0007.253 > 0, invert = T )
Iwant <- subset(Iwant, subset = HyS0001.110 > 0, invert = T )
Iwant # 4023## An object of class Seurat
## 30136 features across 4023 samples within 2 assays
## Active assay: SCT (15068 features, 3000 variable features)
## 1 other assay present: RNA
## 2 dimensional reductions calculated: pca, umapDimPlot(Iwant) # cleaner
# Visualize the distribution of genes detected per cell via histogram
Iwant@meta.data %>%
ggplot(aes(color=sample, x=nGene, fill= sample)) +
geom_density(alpha = 0.2) +
theme_classic() +
scale_x_log10() +
geom_vline(xintercept = 7000)
# sperm problem mitigated, still not perfect.
DimPlot(Iwant, label = T)
VlnPlot(Iwant, "HyS0041.99", assay = "SCT", slot = "data") # very few cell in C16 (zymo GC)
# compare to nematocilin below
VlnPlot(Iwant, "HyS0008.263", assay = "SCT", slot = "data")
#HyS0050.7
VlnPlot(Iwant, "HyS0050.7", assay = "SCT", slot = "data")
aggr_fltd <- Iwantaggr_fltd <- SCTransform(aggr_fltd, vst.flavor = "v2") %>%
RunPCA(npcs = 50) %>%
RunUMAP(dims = 1:15) %>%
FindNeighbors(reduction = "pca", dims = 1:15) %>%
FindClusters(resolution = 0.5) ##
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## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 4023
## Number of edges: 109622
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9387
## Number of communities: 16
## Elapsed time: 0 secondsDimPlot(aggr_fltd,label = T) + NoLegend() + NoAxes()
FeaturePlot(aggr_fltd, features = c("HyS0061.60", "HyS0050.7", #PCNA, #piwi
"HyS0008.263", "HyS0030.203", #Ncol1, Nematocilin
"HyS0041.99", "HyS0004.446", #chitinase,mucs
"HyS0085.53", "HyS0013.338", #ELAV, Rfamide
"HyS0017.253", "HyS0010.323"), #Wnt5, Pitx
order = T, min.cutoff = 'q10')
getwd()## [1] "/Users/jsong/OneDrive - University of Florida/Schnitzler_Lab/Jingwei/Whitney/Manuscript/10X_/Justin2019"# save(aggr_fltd, file="aggr_fltd_PC50_dim15_res.0.5_v2.RData")
# sessionInfo
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] RCurl_1.98-1.12 cowplot_1.1.1
## [3] scales_1.2.1 Matrix_1.5-4.1
## [5] lubridate_1.9.2 forcats_1.0.0
## [7] stringr_1.5.0 dplyr_1.1.2
## [9] purrr_1.0.1 readr_2.1.4
## [11] tidyr_1.3.0 tibble_3.2.1
## [13] ggplot2_3.4.2 tidyverse_2.0.0
## [15] SeuratObject_4.1.3 Seurat_4.3.0
## [17] SingleCellExperiment_1.18.1 SummarizedExperiment_1.26.1
## [19] Biobase_2.56.0 GenomicRanges_1.48.0
## [21] GenomeInfoDb_1.32.4 IRanges_2.30.1
## [23] S4Vectors_0.34.0 BiocGenerics_0.42.0
## [25] MatrixGenerics_1.8.1 matrixStats_1.0.0
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.8 igraph_1.4.3
## [3] lazyeval_0.2.2 sp_1.6-1
## [5] splines_4.2.1 listenv_0.9.0
## [7] scattermore_1.1 digest_0.6.31
## [9] htmltools_0.5.5 fansi_1.0.4
## [11] magrittr_2.0.3 tensor_1.5
## [13] cluster_2.1.4 ROCR_1.0-11
## [15] tzdb_0.4.0 globals_0.16.2
## [17] R.utils_2.12.2 timechange_0.2.0
## [19] spatstat.sparse_3.0-1 colorspace_2.1-0
## [21] ggrepel_0.9.3 xfun_0.39
## [23] jsonlite_1.8.5 progressr_0.13.0
## [25] spatstat.data_3.0-1 survival_3.5-5
## [27] zoo_1.8-12 glue_1.6.2
## [29] polyclip_1.10-4 gtable_0.3.3
## [31] zlibbioc_1.42.0 XVector_0.36.0
## [33] leiden_0.4.3 DelayedArray_0.22.0
## [35] future.apply_1.11.0 abind_1.4-5
## [37] DBI_1.1.3 spatstat.random_3.1-5
## [39] miniUI_0.1.1.1 Rcpp_1.0.10
## [41] viridisLite_0.4.2 xtable_1.8-4
## [43] reticulate_1.30 htmlwidgets_1.6.2
## [45] httr_1.4.6 RColorBrewer_1.1-3
## [47] ellipsis_0.3.2 ica_1.0-3
## [49] farver_2.1.1 R.methodsS3_1.8.2
## [51] pkgconfig_2.0.3 sass_0.4.6
## [53] uwot_0.1.14 deldir_1.0-9
## [55] utf8_1.2.3 labeling_0.4.2
## [57] tidyselect_1.2.0 rlang_1.1.1
## [59] reshape2_1.4.4 later_1.3.1
## [61] munsell_0.5.0 tools_4.2.1
## [63] cachem_1.0.8 cli_3.6.1
## [65] generics_0.1.3 ggridges_0.5.4
## [67] evaluate_0.21 fastmap_1.1.1
## [69] yaml_2.3.7 goftest_1.2-3
## [71] knitr_1.43 fitdistrplus_1.1-11
## [73] RANN_2.6.1 sparseMatrixStats_1.8.0
## [75] pbapply_1.7-0 future_1.32.0
## [77] nlme_3.1-162 mime_0.12
## [79] ggrastr_1.0.2 R.oo_1.25.0
## [81] compiler_4.2.1 rstudioapi_0.14
## [83] beeswarm_0.4.0 plotly_4.10.2
## [85] png_0.1-8 spatstat.utils_3.0-3
## [87] glmGamPoi_1.8.0 bslib_0.5.0
## [89] stringi_1.7.12 highr_0.10
## [91] lattice_0.21-8 vctrs_0.6.2
## [93] pillar_1.9.0 lifecycle_1.0.3
## [95] spatstat.geom_3.2-1 lmtest_0.9-40
## [97] jquerylib_0.1.4 RcppAnnoy_0.0.20
## [99] data.table_1.14.8 bitops_1.0-7
## [101] irlba_2.3.5.1 httpuv_1.6.11
## [103] patchwork_1.1.2 R6_2.5.1
## [105] promises_1.2.0.1 KernSmooth_2.23-21
## [107] gridExtra_2.3 vipor_0.4.5
## [109] parallelly_1.36.0 codetools_0.2-19
## [111] MASS_7.3-60 withr_2.5.0
## [113] sctransform_0.3.5 GenomeInfoDbData_1.2.8
## [115] mgcv_1.8-42 parallel_4.2.1
## [117] hms_1.1.3 grid_4.2.1
## [119] DelayedMatrixStats_1.18.2 rmarkdown_2.22
## [121] Rtsne_0.16 spatstat.explore_3.2-1
## [123] shiny_1.7.4 ggbeeswarm_0.7.2